Weegy: Write good, clean HTML
While your site may appear correctly in some browsers even if your HTML is not valid, there's no guarantee that it will appear correctly in all browsers - or in all future browsers. [ The best way to make sure that your page looks the same in all browsers is to write your page using valid HTML and CSS, and then test it in as many browsers as possible. Clean, valid HTML is a good insurance policy, and using CSS separates presentation from content, and can help pages render and load faster. Validation tools, such as the free online HTML and CSS validators provided by the W3 Consortium, are useful for checking your site, and tools such as HTML Tidy can help you quickly and easily clean up your code. ] (More)

Weegy: common variables with the same data but different attributes
The way SAS handles these differences depends on which attributes are different:
type attribute
If the type attribute is different, [ SAS stops processing the DATA step and issues an error message stating that the variables are incompatible.
To correct this error, you must use a DATA step to re-create the variables. The SAS statements you use depend on the nature of the variable.
length attribute
If the length attribute is different, SAS takes the length from the first data set that contains the variable. In the following example, all data sets that are listed in the MERGE statement contain the variable Mileage. In QUARTER1, the length of the variable Mileage is four bytes; in QUARTER2, it is eight bytes and in QUARTER3 and QUARTER4, it is six bytes. In the output data set YEARLY, the length of the variable Mileage is four bytes, which is the length derived from QUARTER1.
data yearly;
merge quarter1 quarter2 quarter3 quarter4;
by Account;
run;
To override the default and set the length yourself, specify the appropriate length in a LENGTH statement that precedes the SET, MERGE, or UPDATE statement. ] (More)

Weegy: Data redundancy occurs in database systems which have a field that is repeated in two or more tables. [ For instance, in case when customer data is duplicated and attached with each product bought then redundancy of data is a known source of inconsistency, since customer might appear with different values for given attribute.[1] Data redundancy leads to data anomalies and corruption and generally should be avoided by design.[2] Database normalization prevents redundancy and makes the best possible usage of storage.[3] Proper use of foreign keys can minimize data redundancy and chance of destructive anomalies.[2] However sometimes concerns of efficiency and convenience can result redundant data design despite the risk of corrupting the data.[4] ] (More)